Author: Steve Maughan
Date: 06:48:36 11/10/97
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I've some experience of neural networks and I don't think it would be possible to generate a practical evaluation function with them. Although neural networks can cope with non-linearity (eg a knight may or may not be worth 3 points if positioned on e5), the huge range of non-linearity involved in chess positions would defeat the non-linear training algorithms. Another reason for shying away from neural Networks would be the time required to evaluate a position. Due to the complexity of neural networks they usually use floating point maths (but I guess that could be substituted for lookup table) and require the calculation of intermediate values (hidden layers). This would greatly slow down the search compared to a linear evaluation function and would therefore need to give a much more accurate positional evaluation to compensate. Also a complete evaluation will need to be carried out at the leaf nodes since a non-linear element of evaluation may be altered by the last move in the search chain (eg the last move Nf3 alters the value of the Bishop on a7). This means that a program cannot perform incremental evaluation, slowing it down even further. I don't want to sound too negative but I really think it would be tough to develop a chess program based on Neural networks. However Go or Othello are far more likely to be suitable! Regards Steve Maughan
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